Antibiotic resistance complicates the majority of Staphylococcus aureus (S. aureus) infections, as a full two thirds of hospital-associated S. aureus infections and ~50% of those acquired in the community are now methicillin-resistant (MRSA). The increasing incidence of multi-drug resistance in S. aureus and other bacteria underscores the need for next-generation antibiotics capable of combating these dangerous pathogens. While traditional small molecule antibiotics inhibit genetically-encoded intracellular enzymes, an alternative strategy is to employ recombinant versions of natural lytic enzymes such as Staphylococcus simulans lysostaphin (ssLST), which acts by catalytic degradation of the cell wall and may therefore have lower susceptibility to evolved resistance. Unfortunately, as a bacterial protein itself, ssLST is known to drive a potent immune response, providing a major barrier to clinical development of ssLST therapies. This proposal hypothesizes that by integrating novel computational deimmunization algorithms with cutting- edge biomolecular engineering and immunogenicity screening technologies, we can redesign ssLST at the molecular level so as to maintain wild-type stability and catalytic function while simultaneously reducing immunogenicity. Two complementary approaches to developing deimmunized ssLST variants will be pursued in parallel. Aim 1 seeks to computationally design and experimentally evaluate a small number of variants predicted to have simultaneously good activity and reduced immunogenicity. The design algorithms will employ detailed modeling of sequence and structure in order to select optimal sets of deimmunizing mutations. The bactericidal activity of the engineered variants will be quantified by determination of Minimal Inhibitory Concentration (MIC), Minimal Bactericidal Concentration (MBC), and S. aureus lysis kinetics. The immunogenicity of the engineered variants will be assessed in a transgenic mouse model using antibody titers, inflammatory cytokine secretion, and T cell activation as readouts. Aim 2 seeks to computationally design combinatorial libraries predicted to be enriched in variants with reduced immunogenicity, and then employ high-throughput activity screening to identify active variants for further evaluation. The design algorithms will optimize primarily for immunogenicity in selecting mutations for library construction, leaving the screens to identify highly active library members for detailed characterization as in Aim 1. Successfully achieving these aims will result in powerful algorithms for optimizing individual variants and libraries of therapeutic proteins, a broadly applicable fluorescence-based assay enabling ultra-high-throughput screening of genetically-engineered antibacterial proteins, and fully functional, non-immunogenic, anti- staphylococcal biocatalysts potentially useful in treating drug-resistant S. aureus infections.